class_docstr: Calculates the values of a set of features for given instance ids.
code: def _calculate_direct_features(
      self,
      features,
      child_df,
      df_trie,
      progress_callback,
  ):
      path = features[0].relationship_path
      assert (
          len(path) == 1
      ), "Error calculating DirectFeatures, len(path) != 1"
  
      parent_df = df_trie.get_node([path[0]]).value
      _is_forward, relationship = path[0]
      merge_col = relationship._child_column_name
  
      # generate a mapping of old column names (in the parent dataframe) to
      # new column names (in the child dataframe) for the merge
      col_map = {relationship._parent_column_name: merge_col}
      index_as_feature = None
  
      fillna_dict = {}
      for f in features:
          feature_defaults = {
              name: f.default_value
              for name in f.get_feature_names()
              if not pd.isna(f.default_value)
          }
          fillna_dict.update(feature_defaults)
          if (
              f.base_features[0].get_name()
              == relationship._parent_column_name
          ):
              index_as_feature = f
          base_names = f.base_features[0].get_feature_names()
          for name, base_name in zip(f.get_feature_names(), base_names):
              if name in child_df.columns:
                  continue
              col_map[base_name] = name
  
      # merge the identity feature from the parent dataframe into the child
      merge_df = parent_df[list(col_map.keys())].rename(columns=col_map)
      if is_instance(merge_df, (dd, ps), "DataFrame"):
          new_df = child_df.merge(
              merge_df,
              left_on=merge_col,
              right_on=merge_col,
              how="left",
          )
      else:
          if index_as_feature is not None:
              merge_df.set_index(
                  index_as_feature.get_name(),
                  inplace=True,
                  drop=False,
              )
          else:
              merge_df.set_index(merge_col, inplace=True)
  
          new_df = child_df.merge(
              merge_df,
              left_on=merge_col,
              right_index=True,
              how="left",
          )
  
      progress_callback(len(features) / float(self.num_features))
  
      return new_df.fillna(fillna_dict)
  
imports: from datetime import datetime
  from functools import partial
  import numpy
  import pandas
  import pandas.api.types
  from featuretools.entityset.relationship import RelationshipPath
  from featuretools.exceptions import UnknownFeature
  from featuretools.feature_base import AggregationFeature, DirectFeature, GroupByTransformFeature, IdentityFeature, TransformFeature
  from featuretools.utils import Trie
  from featuretools.utils.gen_utils import Library, get_relationship_column_id, import_or_none, is_instance
label_desc: Use image node_id 11 for calling the FeatureSetCalculator obj's underlying member method code with example usage: obj._calculate_direct_features(features, child_df, df_trie, progress_callback) and returns: new_df